Why it’s time to rethink traditional data governance frameworks

A person standing in front of a rack of servers inside a data center
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For organizations everywhere, data has become a vital strategic asset, essential for powering high-velocity decisions, elevating customer satisfaction, and boosting operational efficiency and profits. Providing the vital intelligence organizations need to build long-term strategies, optimize processes, and empower front-line workers, data delivers the contextual insights and evidence needed to support informed decision-making in every function and area of the business.

In today’s digital age, there’s no shortage of organizational data to draw on for in-depth analysis thanks to the growing number of connected devices and the rise of AI. Yet many enterprises struggle to harness these data assets and leverage this information effectively and efficiently. And that’s proving a major stumbling block to creating a data-driven culture that delivers the business value the organization needs.

Paolo Platter

CTO & Co-Founder, Agile Lab.

Harnessing a complex and growing data ecosystem

The potential benefits of taking a deep dive into large data sets to make better decisions are well documented. However, many businesses find it difficult to jump-start their data-driven roadmap, despite investing heavily in data management technology and tools. As a consequence, they are stuck collecting huge volumes of data in the hope that, at some point, they will be able to unlock its latent value.

Becoming a truly data-driven enterprise depends on addressing some key challenges, from aligning objectives with strategy to deliver desired insights, to ensuring that data lakes and warehouses that are overflowing with untapped potential are better utilised to deliver actional business intelligence.

Most significant of all though, data governance has emerged as a critical enabler for facilitating the effective management and utilization of data across the enterprise. Yet, many organizations are finding it difficult to align their data governance efforts with real-world business goals and ensure that the right people are able to access the right data at the right time. In an increasingly data-driven world, organizations will need to rethink their governance frameworks if they want to manage and utilize data effectively.

The pressing need to redefine data governance

Today’s large enterprises have a massive data footprint but to make better business decisions, business leaders need to know what insights their data can reveal. Key to this process is good governance that ensures data can be trusted and is secure and available. And therein lies the rub.

Many organizations are finding it impossible to enforce strict governance frameworks that ensure data is consumed and produced in alignment with internal standards for quality, integrity, architecture, compliance, and security. A situation that is further complicated by disparate systems, formats, and locations that make it laborious to access, consolidate, process, and apply consistent governance standards without engaging in considerable manual intervention.

Having deployed a combination of sophisticated tools and highly skilled staff to extract meaningful intelligence from their assets, enterprises find they still face painfully long lead times that can stretch into months and years. Unsurprisingly, frustrations around the difficulties associated with harnessing data assets and pushing insights out to users are mounting.

To bridge the gap between data governance objectives and effective implementation, organizations will need to go beyond standardized processes and define a new data governance approach.

Regaining control with computational governance

What’s needed is a reliable method of imposing enterprise-wide governance rules that, like guardrails, remain in place throughout the lifecycle of data no matter where it resides. This is where the concept of distributed computational governance comes into play.

Unlike data management tools that create, copy, and move data around, computational governance is an approach that imposes a consistent, automated governance framework throughout the enterprise. It’s a revolutionary approach that enables enterprises to enforce internal standards and security controls while empowering data consumers and producers to expedite data discovery and project development.

With computational governance, enterprises can rapidly realise the potential value of their existing data with no need for further consolidation. Overseeing all data tools and technologies, rather than replacing them, computational governance is a technology-agnostic game-changer that automates governance processes and ensures compliance with policies and regulations.

Inbuilt customisable guardrails ensure that every project adheres to relevant standards at global and local levels and cannot reach production unless pre-defined policies are followed. This encompasses everything from data quality, integrity, and architecture to compliance and security. Since bypassing the system isn’t an option, relying on trust for adherence is a thing of the past.

Plus, computational governance future-proofs how organizations unlock their data-driven potential, enabling them to adopt new tools as required and bring in structured and unstructured data as business demands evolve.

Revolutionizing business performance and agility

A computational governance approach enables data practitioners to eradicate time-consuming tasks such as finding and validating the integrity of data before initiating projects.

Data teams simply create and customize specifications that set out all required data practices, internal policies, compliance rules, and architecture standards. Intelligent templates help data practitioners automate any technology and practice, cutting the delivery of new and existing projects from years to months.

Plus, a user-friendly interface means that users with the right permissions can search and retrieve business-relevant information with zero technical assistance from data managers. All of which supports faster time-to-market and radically improves enterprise responsiveness to new opportunities or market changes by distributing data ownership to business domains. But that’s not all.

Unleashing the data-driven enterprise

Addressing the shortcomings of traditional frameworks and paving the way for a more agile, reliable, and cost-effective governance model, a computational governance approach simplifies how enterprises transition to distributed data mesh architecture models that treat data like a product and organize data by specific business domain.

Providing all the consistency and control needed to power up a fully functional mesh model that gives domain experts the freedom to work with the data they know best, computational governance delivers against multiple enterprise needs: domain-oriented ownership; self-service data infrastructure as a platform that democratizes the ability to access and act on data; federated governance; and data-as-a-product.

Computational governance delivers the guardrails today’s enterprises need to break down data silos, ensure compliance and security standards are maintained, and give domain experts autonomy to unlock the full potential of their data assets.

By making it possible to balance governance, control, and performance so that data becomes a genuine business enabler, computational governance addresses the shortcomings of traditional frameworks that stand in the way of becoming a truly data-driven enterprise.

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Paolo Platter, CTO & Co-Founder, Agile Lab.